永磁同步电机伺服调速控制系统研究

发布时间：2018-03-03 06:17

本文选题：永磁同步电机　切入点：伺服调速　出处：《兰州交通大学》2017年硕士论文　论文类型：学位论文

【摘要】：PMSM(Permanent Magnet Synchronous Motor,永磁同步电机)具有效率高、转矩脉动小、动态响应好、调速范围宽等优点,因而在国防、工业、交通以及生活等领域运用广泛。PMSM伺服调速控制系统通常采用VC(Vector Control,矢量控制)和DTC(Direct Torque Control,直接转矩控制)方法。VC转矩响应速度慢,还受转子参数变化的影响,DTC方法简单,但存在较大转矩脉动。PMSM伺服调速控制系统中,传统的PI控制器参数鲁棒性较差,无法满足系统动态响应速度和稳态精度的要求。目前非线性系统的MPC(Model Predictive Control,模型预测控制)理论研究比较活跃,本文采用神经网络(Neural Network,NN)、支持向量机(Support Vector Machine,SVM)分别与MPC相结合,在矢量控制基础上,研究设计PMSM伺服调速控制系统的MPC结构与算法,充分发挥MPC的优势,以提高PMSM伺服调速控制系统的整体性能。本文研究内容主要包括以下几个方面:(1)针对常规PI控制的PMSM调速系统中,PI参数鲁棒性差的问题,对其进行改进。第一,针对工程设计方法整定PI控制器参数不是最优的缺点,采用SOA(Seeker Optimization Algorithm,人群搜索算法)算法对速度控制器参数进行优化;第二,采用神经网络PI控制。当系统的运行状态发生变化时,利用神经网络自动调节PI控制器的参数,通过神经网络的自学习、加权系数的调整,使PI控制的参数达到最优。(2)考虑到PMSM伺服调速系统的非线性和不确定性,根据MPC的基本原理,利用RBF(Radial Basis Function,径向基函数)神经网络建立PMSM的预测模型,在矢量控制基础上,研究设计RBF神经网络预测控制的PMSM调速系统。根据多步预测控制的方法建立PMSM的预测模型,采用梯度下降法优化求解最优控制量,提高了伺服调速系统的鲁棒性和动态性能。(3)利用LS-SVM(Least Square Support Vector Machine,最小二乘支持向量机)建立PMSM的预测模型,在矢量控制基础上,研究设计LS-SVM预测控制的PMSM调速系统。研究分析LS-SVM用于回归预测的原理和方法,采用牛顿法设计非线性控制器,从而提高了PMSM伺服调速系统的响应速度和稳态精度。(4)分析研究数控机床伺服进给系统组成结构,建立机械传动部分的动力学模型。将PI控制和RBF神经网络预测控制的PMSM速度控制系统应用于CK6136型数控车床进给伺服系统,实现了工作台位置的精确控制。
[Abstract]:PMSM(Permanent Magnet Synchronous Motor (PMSM) has the advantages of high efficiency, small torque ripple, good dynamic response and wide speed range. VC(Vector control (vector control) and DTC(Direct Torque control (direct torque control) methods are usually used in traffic and daily life. The torque response speed of VC is slow, and it is simple to be affected by rotor parameters. However, in the large torque ripple. PMSM servo speed control system, the traditional Pi controller has poor robustness. The theory of MPC(Model Predictive control (model predictive control) for nonlinear systems is active at present. In this paper, neural network and support Vector machine are used to combine with MPC, respectively. On the basis of vector control, the MPC structure and algorithm of PMSM servo speed control system are studied and designed to give full play to the advantages of MPC. In order to improve the overall performance of PMSM servo speed control system, this paper mainly includes the following aspects: 1) aiming at the problem of poor robustness of Pi parameters in conventional Pi controlled PMSM speed control system, we improve it. Aiming at the disadvantage that the parameter of Pi controller is not optimal in engineering design method, SOA(Seeker Optimization algorithm (crowd search algorithm) algorithm is used to optimize the parameters of speed controller. When the running state of the system changes, the parameters of the Pi controller are automatically adjusted by the neural network, and the weighting coefficient is adjusted by the self-learning of the neural network. Considering the nonlinearity and uncertainty of PMSM servo speed control system, according to the basic principle of MPC, using RBF(Radial Basis function (radial basis function) neural network to establish the prediction model of PMSM, and on the basis of vector control, the prediction model of PMSM is established. The PMSM speed regulation system based on RBF neural network predictive control is studied and designed. According to the method of multistep predictive control, the prediction model of PMSM is established, and the optimal control quantity is optimized by gradient descent method. The robustness and dynamic performance of servo speed control system are improved. The prediction model of PMSM is established by using LS-SVM(Least Square Support Vector Machine (LS-SVM). This paper studies and designs the PMSM speed regulating system based on LS-SVM predictive control, analyzes the principle and method of LS-SVM used in regression prediction, and designs a nonlinear controller by Newton method. Thus, the response speed and steady-state precision of PMSM servo speed control system are improved. The PMSM speed control system based on Pi control and RBF neural network predictive control is applied to the feed servo system of CK6136 NC lathe, and the precise control of the position of the worktable is realized.
【学位授予单位】：兰州交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP273;TM341

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